title: Automatic detection of basal cell carcinoma using telangiectasia analysis in dermoscopy skin lesion images.
abstract: BACKGROUND
  Telangiectasia, dilated blood vessels near the surface of the skin of small, varying diameter, are critical dermoscopy structures used in the detection of basal cell carcinoma (BCC). Distinguishing these vessels from other telangiectasia, that are commonly found in sun-damaged skin, is challenging.
  
  
  METHODS
  Image analysis techniques are investigated to find vessels structures in BCC automatically. The primary screen for vessels uses an optimized local color drop technique. A noise filter is developed to eliminate false-positive structures, primarily bubbles, hair, and blotch and ulcer edges. From the telangiectasia mask containing candidate vessel-like structures, shape, size and normalized count features are computed to facilitate the discrimination of benign skin lesions from BCCs with telangiectasia.
  
  
  RESULTS
  Experimental results yielded a diagnostic accuracy as high as 96.7% using a neural network classifier for a data set of 59 BCCs and 152 benign lesions for skin lesion discrimination based on features computed from the telangiectasia masks.
  
  
  CONCLUSIONS
  In current clinical practice, it is possible to find smaller BCCs by dermoscopy than by clinical inspection. Although almost all of these small BCCs have telangiectasia, they can be short and thin. Normalization of lengths and areas helps to detect these smaller BCCs.
authors.affiliation: Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA.
authors.forename: Beibei
authors.initials: B
authors.lastname: Cheng
authors.forename: David
authors.initials: D
authors.lastname: Erdos
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Stanley
authors.forename: William V
authors.initials: WV
authors.lastname: Stoecker
authors.forename: David A
authors.initials: DA
authors.lastname: Calcara
authors.forename: David D
authors.initials: DD
authors.lastname: Gómez
mesh_terms: D000465:Algorithms; D002280:Basal Cell Carcinoma / Q000150:complications / Q000473:pathology*; D046169:Dermoscopy / Q000379:methods*; D005431:Florida; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008923:Missouri; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000150:complications / Q000473:pathology*; D013684:Telangiectasis / Q000209:etiology / Q000473:pathology*
keywords: basal cell carcinoma; dermoscopy; image analysis; neural network; telangiectasia; vessels